feat: initial HSAP platform

Huaxu Sentinel Active Safety Platform with embedded algorithm code,
Docker Compose setup, and vendored dataset scaffolds for clone-and-run.

Co-authored-by: Cursor <cursoragent@cursor.com>

algorithms/dms_yolo/code/rknn_export/convert.py

@@ -0,0 +1,89 @@
+import sys
+import argparse
+import os
+from rknn.api import RKNN
+
+
+def default_dataset_path():
+    # Prefer model zoo COCO subset (images included); fallback to local list.
+    here = os.path.dirname(os.path.abspath(__file__))
+    zoo_txt = os.path.abspath(os.path.join(here, '../../../../BK2/rknn_model_zoo-main/datasets/COCO/coco_subset_20.txt'))
+    local_txt = os.path.join(here, 'datasets/COCO/coco_subset_20.txt')
+    return zoo_txt if os.path.isfile(zoo_txt) else local_txt
+
+
+def parse_arg():
+    parser = argparse.ArgumentParser(description='Convert ONNX model to RKNN format')
+    
+    parser.add_argument('--model-path', type=str, required=True,
+                        help='Path to the ONNX model file')
+    parser.add_argument('--platform', type=str, required=True,
+                        choices=['rk3562', 'rk3566', 'rk3568', 'rk3576', 'rk3588', 'rv1126b', 'rv1109', 'rv1126', 'rk1808'],
+                        help='Target platform. Choose from: [rk3562, rk3566, rk3568, rk3576, rk3588, rv1126b, rv1109, rv1126, rk1808]')
+    parser.add_argument('--dtype', type=str, default='i8',
+                        choices=['i8', 'u8', 'fp'],
+                        help='Data type for quantization (i8/u8 for quantized, fp for float). '
+                             'Choose [i8, fp] for [rk3562, rk3566, rk3568, rk3576, rk3588, rv1126b]; '
+                             'Choose [u8, fp] for [rv1109, rv1126, rk1808]. Default: i8')
+    parser.add_argument('--rknn-path', type=str, default=None,
+                        help='Output path for RKNN model. Default: ./<model_name>.rknn')
+    parser.add_argument('--data-path', type=str, default=default_dataset_path(),
+                        help='Path to dataset file for quantization')
+    parser.add_argument('--batch-size', type=int, default=1,
+                        help='Batch size for RKNN model. Default: 1')
+    
+    args = parser.parse_args()
+    
+    model_path = args.model_path
+    platform = args.platform
+    dataset_path = args.data_path
+    batch_size = args.batch_size
+    # Determine quantization based on dtype
+    do_quant = args.dtype in ['i8', 'u8']
+    
+    # Determine output path
+    if args.rknn_path:
+        output_path = args.rknn_path
+    else:
+        output_path = os.path.join('./', os.path.basename(model_path).split('.')[0] + '.rknn')
+    
+    return model_path, platform, do_quant, output_path, dataset_path, batch_size
+
+if __name__ == '__main__':
+    model_path, platform, do_quant, output_path, dataset_path, batch_size = parse_arg()
+
+    # Create RKNN object
+    rknn = RKNN(verbose=False)
+
+    # Pre-process config
+    print('--> Config model')
+    rknn.config(mean_values=[[0, 0, 0]], std_values=[[255, 255, 255]], target_platform=platform)
+    print('done')
+
+    # Load model
+    print('--> Loading model')
+    ret = rknn.load_onnx(model=model_path)
+    if ret != 0:
+        print('Load model failed!')
+        exit(ret)
+    print('done')
+
+    # Build model
+    print('--> Building model')
+    ret = rknn.build(do_quantization=do_quant, dataset=dataset_path, rknn_batch_size=batch_size)
+    if ret != 0:
+        print('Build model failed!')
+        exit(ret)
+    print('done')
+
+    # Export rknn model
+    print('--> Export rknn model')
+    ret = rknn.export_rknn(output_path)
+    if ret != 0:
+        print('Export rknn model failed!')
+        exit(ret)
+    print('done')
+    print('rknn model saved to: ', output_path)
+
+    # Release
+    rknn.release()